This invention relates to geophysical prospecting using seismic signals, and in particular to systems and methods for re-gridding seismic data.
Effectively searching for oil and gas reservoirs often requires imaging the reservoirs using three-dimensional (3-D) seismic data. Seismic data are recorded at the earth""s surface or in wells, and an accurate model of the underlying geologic structure is constructed by processing the data. 3-D seismic imaging is perhaps the most computationally intensive task facing the oil and gas industry today. The size of typical 3-D seismic surveys can be in the range of hundreds of gigabytes to tens of terabytes of data. Processing such large amounts of data often poses serious computational challenges.
Obtaining high-quality earth images necessary for contemporary reservoir development and exploration is particularly difficult in areas with complex geologic structures. In such regions, conventional seismic technology may either incorrectly reconstruct the position of geological features or create no usable image at all. Moreover, as old oil fields are depleted, the search for hydrocarbons has moved to smaller reservoirs and increasingly hostile environments, where drilling is more expensive. Advanced imaging techniques capable of providing improved knowledge of the subsurface detail in areas with complex geologic structures are becoming increasingly important.
In a typical seismic survey, elastic (seismic) waves are propagated into the earth region of interest. The elastic waves may be generated by various types of sources such as dynamite, air guns, and hydraulic vibrators, situated along the earth""s surface. As these waves propagate downward through the earth, portions of their energy are sent back to the earth""s surface by reflection and refraction which occur whenever abrupt changes in impedance are encountered. The reflected and/or refracted seismic waves are recorded at the earth""s surface or in wellbores by an array of receivers such as geophones, hydrophones, or other similar devices. The underlying earth structure can be imaged by appropriate processing of the signals returned to the receivers.
Raw seismic data as recorded are generally not readily interpretable. While such data show the existence of formation interfaces, raw data do not accurately inform the interpreter as to the location of these interfaces. The process of migration, also called imaging, repositions the seismic data so that a more accurate picture of subsurface reflectors is given. In order to perform migration calculations, the seismic velocities of the subsurface at a multitude of points are first determined, commonly by performing migration velocity analysis (MVA). A two- or three-dimensional spatial distribution of subsurface velocity forms a velocity model for the subsurface region of interest. A large-scale velocity model covering the extent of the seismic data acquisition volume can be a complicated structure with vertically and laterally varying velocity.
Prior to imaging, it is often desirable to arrange data into a geometry different from the original data recording geometry. Such rearrangement or re-gridding may be desired in order to enhance the performance of given imaging algorithms, comply with physical requirements or assumptions of such imaging algorithms, for purposes of standardization or convenience, or for facilitating comparisons to other data sets.
Known methods of re-arranging seismic data include sorting, binning, dip moveout (DMO), partial stacking, offset continuation, and azimuth moveout (AMO). Sorting involves re-ordering the data along trace coordinates. Common sorting methods include CDP sorting, common offset sorting, common shot sorting, and common receiver sorting. Sorting does not fundamentaly change the character or nature of the data, and is not typically useful for filling-in data gaps. Binning involves re-assigning the coordinates of individual traces. The binning process may be extended to flex binning, a process in which multiple input traces may contribute to given output traces. Binning may also include amplitude normalization. A normal moveout correction may be performed prior to binning, in order to improve accuracy by handling first-order offset-dependent velocity variation effects. Inverse normal moveout can be applied after binning if such a correction is performed. Binning can be inaccurate in the presence of dipping geological strata, or when the binned grid differs substantially from the original grid. Moreover, binning may not adequately preserve diffractions. Partial stacking includes performing normal moveout of the data, and then stacking the data over predefined offset ranges. The data are inverse-normal-moveout corrected to generate data along a new output grid. Partial stacking can be inaccurate in the presence of significant lateral velocity variations and dipping geological strata.
Dip moveout (DMO) and partial migration operators have also been used to re-grid seismic data. Such methods change the fundamental nature of the seismic data. For further information on DMO see for example U.S. Pat. Nos. 4,878,204, 5,285,422, and 5,719,822. Offset continuation, another method of re-gridding seismic data, involves combining the forward and inverse DMO operators in two dimensions. For more information on offset continuation see for example the article by Bagaini et al., xe2x80x9c2-D continuation operators and their applications,xe2x80x9d Geophysics 61(6): 1846-1858, 1996.
Azimuth moveout (AMO) combines the forward and inverse DMO operators in three dimensions, as described in the article by Biondi et al., xe2x80x9cAzimuth Moveout for 3-D Prestack Imaging,xe2x80x9d Geophysics 63(2):574-588, 1998. AMO is a wave-equation correct re-gridding algorithm that handles dipping geological strata and variable velocity relatively accurately. Biondi et al""s formulation of AMO is as an input-based superposition algorithm. The implementation requires input data to be sorted into constant-offset sections. For each input trace, an AMO surface is constructed. These AMO surfaces are superimposed to create the AMO-transformed output volume for a given output offset volume. For sampling and antialiasing reasons, their implementation also involves transforming the data coordinates to a processing coordinate system. For arbitrary collections of field data, Biondi et al.""s requirement of sorting into common offsets can be time consuming and cumbersome. The input-oriented approach can also be generally less efficient for most real data geometries.
The present invention provides a method of re-gridding seismic data, comprising: establishing an input seismic data set corresponding to an input grid, the input data set comprising a plurality of input traces each corresponding to an input location; choosing an output grid comprising a plurality of output locations; and generating a re-gridded output seismic data set corresponding to the output grid, the output data set comprising a plurality of output traces corresponding to the plurality of output locations. Generating the output data set comprises performing output-based azimuth moveout to generate an output trace corresponding to each output location by: identifying a subset of input locations contributing to the output trace, the subset of input locations comprising a summation trajectory for the output trace, wherein at least one of the input locations along the summation trajectory has a different azimuth and midpoint from said each output location; computing a contribution of each of the input traces along the summation trajectory to the output trace; and summing the contribution of said each of the input traces into the output trace. The present invention further provides apparatus and computer-readable media encoding instructions to perform the methods described herein.